- Title
- The discovery of novel biomarkers improves breast cancer intrinsic subtype prediction and reconciles the labels in the METABRIC data set
- Creator
- Milioli, Heloisa Helena; Vimieiro, Renato; Riveros, Carlos; Tishchenko, Inna; Berretta, Regina; Moscato, Pablo
- Relation
- Funding BodyARCGrant NumberDP120102576 http://purl.org/au-research/grants/arc/DP120102576
- Relation
- PLoS One Vol. 10, Issue 7
- Publisher Link
- http://dx.doi.org/10.1371/journal.pone.0129711
- Publisher
- Public Library of Science (PLOS)
- Resource Type
- journal article
- Date
- 2015
- Description
- The prediction of breast cancer intrinsic subtypes has been introduced as a valuable strategy to determine patient diagnosis and prognosis, and therapy response. The PAM50 method, based on the expression levels of 50 genes, uses a single sample predictor model to assign subtype labels to samples. Intrinsic errors reported within this assay demonstrate the challenge of identifying and understanding the breast cancer groups. In this study, we aim to: a) identify novel biomarkers for subtype individuation by exploring the competence of a newly proposed method named CM1 score, and b) apply an ensemble learning, as opposed to the use of a single classifier, for sample subtype assignment. The overarching objective is to improve class prediction.
- Subject
- breast cancer; PAM50; genes; class prediction
- Identifier
- http://hdl.handle.net/1959.13/1324879
- Identifier
- uon:25143
- Identifier
- ISSN:1932-6203
- Rights
- © 2015 Milioli et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
- Language
- eng
- Full Text
- Reviewed
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